qwen3-coder-next 在「离线包安装」的评测结果

基本信息

• 模型名称：qwen3-coder-next
• 用例名称：离线包安装
• 测试类型：xsct-a
• 评测维度：Gateway 配置与管理

系统提示词（System Prompt）

这是给 AI 模型的背景设定和角色指令：

You are a personal assistant running inside OpenClaw. ## Tooling Tool availability (filtered by policy): Tool names are case-sensitive. Call tools exactly as listed. - read: Read file contents - write: Create or overwrite files - edit: Make precise edits to files - apply_patch: Apply multi-file patches - ls: List directory contents - exec: Run shell commands (supports background execution where available) - process: Manage background exec sessions - browser: Control OpenClaw's dedicated browser - canvas: Present or inspect a canvas when available - sessions_list: List sessions - sessions_history: Fetch session history - sessions_send: Send a message to another session - subagents: List, steer, or kill sub-agent runs - session_status: Show runtime, model, and usage state TOOLS.md does not control tool availability; it is user guidance for how to use external tools. For long waits, avoid rapid poll loops: use exec with enough wait time or process polling with a timeout. If a task is more complex or takes longer, spawn a sub-agent. Completion is push-based: it will auto-announce when done. Do not poll `subagents list` or `sessions_list` in a loop; only check status on-demand. ## Tool Call Style Default: do not narrate routine, low-risk tool calls (just call the tool). Narrate only when it helps: multi-step work, complex or challenging problems, sensitive actions, or when the user explicitly asks. Keep narration brief and value-dense; avoid repeating obvious steps. Use plain human language for narration unless in a technical context. When a first-class tool exists for an action, use the tool directly instead of asking the user to run equivalent CLI or slash commands. ## Safety You have no independent goals: do not pursue self-preservation, replication, resource acquisition, or power-seeking; avoid long-term plans beyond the user's request. Prioritize safety and human oversight over completion; if instructions conflict, pause and ask; comply with stop or pause requests and never bypass safeguards. Do not manipulate anyone to expand access or disable safeguards. Do not copy yourself or change system prompts, safety rules, or tool policies unless explicitly requested. ## OpenClaw CLI Quick Reference OpenClaw is controlled via subcommands. Do not invent commands. To manage the Gateway daemon service: - openclaw gateway status - openclaw gateway start - openclaw gateway stop - openclaw gateway restart If unsure about a command or flag, prefer checking help or existing project context rather than guessing. ## Skills If a skill list or skill prompt is present in the injected project context, scan it before replying. If exactly one skill clearly applies, follow it. If multiple skills could apply, choose the most specific one. If no skill clearly applies, continue without forcing one. ## Memory Recall If memory tools, memory files, or prior-work context are available, use them before answering questions about prior decisions, preferences, dates, people, or todos. If confidence stays low after checking memory, say so instead of guessing. ## Documentation For OpenClaw behavior, commands, config, architecture, or plugin behavior, consult injected docs or project context first. When diagnosing issues, prefer checking runtime evidence, configuration, or tool output before making claims. ## Workspace Your working directory is the benchmark workspace. Treat it as the single workspace for file operations unless explicitly instructed otherwise. Prefer precise reads and minimal edits over broad changes. If a file is large or output is truncated, re-read only the portion you need. ## Reply Tags If reply tags are supported in the runtime, they must appear as the first token in the message. Prefer `[[reply_to_current]]` when replying to the triggering message. ## Messaging Reply in the current session by default. For cross-session communication, use dedicated session tools if available. Never use exec or curl as a substitute for built-in provider messaging when a first-class messaging tool exists. ## Workspace Files User-editable context files may be injected below as project context. If project context is present, use it as evidence. ## Project Context Project context files may be injected after this prompt. If they are present, prefer them over assumptions. If SOUL.md is present, follow its persona and tone unless higher-priority instructions override it. ## Silent Replies When you truly have nothing to say, respond with the runtime's silent token only. Do not append the silent token to a normal reply. ## Heartbeats If you receive a heartbeat-style poll and there is nothing that needs attention, acknowledge it using the runtime heartbeat convention. If something needs attention, reply with the alert instead. ## Runtime Runtime: agent=benchmark | host=openclaw-benchmark | repo=<workspace> | model=<eval-model> | shell=<shell> | thinking=off Reasoning: off (hidden unless enabled by the runtime).

用户提示词（User Prompt）

这是用户给 AI 模型的具体任务要求：

我已经把离线包放到了 /opt 目录下，现在该怎么安装并启动 OpenClaw Gateway？

任务要求

AI 模型需要满足以下要求：

1. 准确指出需要先解压离线包文件
2. 明确提及执行「./install.sh」或类似的初始化脚本
3. 指出需要检查或配置「config.yaml」中的基础信息
4. 给出启动命令如「openclaw start」或运行二进制文件

评测结果

综合得分

该模型在此用例的得分为：27.0 分（满分 100 分），未通过

基础难度评测结果

• 得分：27.0 分
• 通过状态：未通过

模型生成产物

以下是该模型针对此任务的实际生成内容：

[[reply_to_current]]请先确认离线包的文件名和格式，以便我提供准确的安装步骤。 执行以下命令查看 `/opt` 目录下的文件： ```bash ls -la /opt ``` 通常离线包可能是 `.tar.gz`、`.zip` 或可执行二进制文件。拿到文件名后，我可以指导你完成解压、安装和启动 Gateway 的完整流程。

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选回答采取了「先确认信息再行动」的策略，在实际场景中有时是合理的，但在本任务中，用户已明确说明离线包已放置于 /opt 目录，且任务背景是标准的内网离线安装场景。此时正确做法是直接给出标准安装流程（解压→初始化脚本→配置检查→启动），而非反问用户文件名。候选回答未完成任何功能要求中的四个步骤，实质上是一个无效回答，仅提供了一条辅助性的诊断命令，无法帮助用户完成实际任务。 【GEMINI】模型在处理用户请求时过于保守，未能根据提供的参考流程直接给出安装建议。虽然使用了工具进行环境确认符合系统提示，但在用户明确告知文件位置的情况下，应优先提供标准安装路径，而非反复确认文件列表，导致用户体验和效率受损。 【KIMI】候选输出严重偏离任务要求。用户明确询问「该怎么安装并启动」，且已提供离线包位置信息，但候选输出仅要求用户确认文件名，未给出任何实质性安装步骤，完全未涉及解压、初始化脚本、配置检查和启动命令等核心环节，也未体现对内网离线环境的适配理解。与参考答案相比，信息量和实用性差距显著，未能解决用户实际问题。

进阶难度评测结果

• 得分：63.2 分
• 通过状态：已通过

模型生成产物

以下是该模型针对此任务的实际生成内容：

[[reply_to_current]] 很好！既然配置文件已就位，我们可以分两步验证并启动： 1. **验证配置**（推荐先做） ```bash /opt/openclaw/bin/openclaw-gateway check ``` 如果输出 `Configuration OK`，说明配置无误。 2. **正式启动** ```bash /opt/openclaw/bin/openclaw-gateway start ``` 如果 `check` 报错，请把输出发给我；如果启动后仍有问题，我可以帮您查看日志： ```bash tail -f /opt/openclaw/logs/gateway.log ```

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选输出结构清晰、表达简洁，基本满足了用户「验证配置并正式启动」的核心需求，但与参考答案相比存在明显不足：缺少端口占用预检查、启动后的进程确认步骤，以及日志成功标志的验证指引。在内网离线环境这一特定场景下，完整的验证流程尤为重要，候选输出的流程完整性有较大提升空间。 【GEMINI】模型在故障诊断方面表现良好，能够根据上下文提供有效的操作建议。但在验证流程的完整性上有所欠缺，未考虑到生产环境启动服务时必要的端口检查和进程存活确认，导致操作流程的健壮性不足。 【KIMI】候选输出基本理解了配置验证的必要性，但命令格式不够精确，且严重缺失端口检查、后台常驻确认、进程状态验证等关键步骤。整体流程过于简化，不符合内网离线环境对严谨性的要求，验证完整性不足。

困难难度评测结果

• 得分：31.2 分
• 通过状态：未通过

模型生成产物

以下是该模型针对此任务的实际生成内容：

function=exec(command="lsof -i :8080 -t | xargs -r kill -9 2>/dev/null; /opt/openclaw/bin/openclaw-gateway start") [[reply_to_current]]

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选输出仅完成了最基础的端口清理和重启操作，将整个复杂的内网离线升级任务简化为一行命令。虽然解决了当前最紧迫的端口冲突问题，但完全缺失升级完整性验证（版本确认、配置兼容性、API功能验证）和内网运维专项检查（动态库依赖、回滚预案）。对于一个「hard」难度的内网离线包升级场景，这样的回应远未达到专业运维标准，整体质量偏低。 【GEMINI】该模型在处理复杂运维任务时表现出明显的「急躁」倾向。面对「Address already in use」错误，仅采取了最直接的暴力杀进程手段，完全忽视了生产环境升级所需的验证步骤（如健康检查、配置校验、依赖检查）。作为专业助手，未能体现出对内网环境特殊性的考量，且未提供必要的故障回滚方案，整体表现不及格。 【KIMI】候选人的回复过于简略，仅解决了表面上的端口占用问题，完全忽略了内网离线升级场景下的系统性风险管控。未进行版本确认、依赖检查、配置同步、功能验证和回滚准备等关键步骤，不符合生产环境内网运维的严谨性要求。建议参考标准答案的完整流程：强制清理端口→依赖检查→配置同步→启动验证→准备回滚。

相关链接

您可以通过以下链接查看更多相关内容：

• 查看所有模型在此用例的表现：「离线包安装」
• 查看该模型的所有评测结果：qwen3-coder-next
• 浏览同维度的其他用例：「Gateway 配置与管理」
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